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Record W4417292616 · doi:10.33540/3324

What goes around comes around

2025· dissertation· W4417292616 on OpenAlexaff
Matteo Buffoni

Bibliographic record

Venuenot available
Typedissertation
Language
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsResistomeAgricultureNexus (standard)Resistance (ecology)Antibiotic resistanceOne HealthTransmission (telecommunications)BiosecurityMobile genetic elements

Abstract

fetched live from OpenAlex

The escalating crisis of antimicrobial resistance (AMR), characterized as a "silent pandemic" responsible for an estimated 1.14 million deaths globally in 2021, demands a rigorous One Health framework. This approach acknowledges that AMR dynamics are driven by deep interconnectedness between human, animal, and environmental domains. Specifically, the farm animal-human interface represents a critical nexus for the dissemination of plasmid-mediated antibiotic resistance. This research aims to bridge the gap between broad ecological factors prevalent in agricultural settings and the precise molecular mechanisms governing the movement and establishment of resistance plasmids from livestock reservoirs to human-associated bacteria. Central to this dissemination is the gut microbiome, which acts as a vast biological reservoir for antimicrobial resistance genes (ARGs). These genes are frequently carried on mobile genetic elements (MGEs), particularly plasmids, which facilitate rapid horizontal gene transfer (HGT) via conjugation. To elucidate how agricultural inputs influence this reservoir, this research first examined the impact of diverse farming practices. Investigations revealed contrasting ecological outcomes: while the administration of Black Soldier Fly Larvae (BSFL) oil in pigs proved ecologically neutral, exerting minimal impact on the fecal microbiome, anticoccidial strategies in chickens imposed strong selective pressures. These pressures significantly reshaped the microbial landscape and co-selected for specific resistome profiles, illustrating how agricultural management can actively modulate the pool of mobile ARGs. Transitioning from ecological observation to molecular dissection, the study focused on the cross-species transmission of the high-priority IncI1-blaCTX-M-1 plasmid. In vitro experiments demonstrated that host origin is not an immediate barrier to HGT; the plasmid transferred efficiently between avian and human Escherichia coli isolates. However, a pivotal distinction emerged between initial transfer and long-term establishment. Post-transfer analyses revealed that the plasmid exhibited significantly lower stability in human hosts compared to avian hosts. This instability triggered rapid genetic adaptations, including large-scale deletions of metabolically costly regions such as conjugation machinery. This phenomenon, identified here as a "paradox of plasmid refinement," indicates that while adaptive changes may aid immediate survival by reducing metabolic burden, they can inadvertently compromise the plasmid’s long-term genomic integrity and transmissibility. In conclusion, this research offers a multi-scale perspective on AMR dissemination. It illustrates that while agricultural practices act as potent ecological drivers creating reservoirs of mobile ARGs, the journey from farm to human is not a simple linear path. Successful plasmid transfer across species barriers does not automatically equate to establishment. Instead, persistence in a novel host is a probabilistic process governed by plasmid identity, host compatibility, and adaptive evolutionary trade-offs. These findings emphasize the existence of natural, albeit imperfect, biological buffers that impede the establishment of livestock-associated resistance in humans, underscoring the need for One Health strategies that account for both ecological selective pressures and molecular establishment barriers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.095
Threshold uncertainty score0.316

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0080.007
Scholarly communication0.0150.012
Open science0.0010.007
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0950.038

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.349
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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